Summary of Recursive Gaussian Process State Space Model, by Tengjie Zheng et al.
Recursive Gaussian Process State Space Model
by Tengjie Zheng, Lin Cheng, Shengping Gong, Xu Huang
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed recursive Gaussian Process State-Space Models (GPSSMs) method offers an efficient solution for online learning in scenarios where prior information is limited. The approach combines first-order linearization with Bayesian updates, enabling closed-form and domain-independent learning. An online selection algorithm selects inducing points based on informative criteria, promoting lightweight learning. Additionally, the method recovers historical measurement information from the current filtering distribution to support online hyperparameter optimization. Compared to state-of-the-art online GPSSM techniques, this method demonstrates superior accuracy, computational efficiency, and adaptability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way of learning dynamical models from data that can be used in different scenarios. It’s like a recipe for making predictions about things that change over time. The method is based on an idea called Gaussian Process State-Space Models (GPSSMs) which are good at finding patterns in noisy data. But until now, there wasn’t an efficient way to use this approach when you don’t have much information beforehand. This paper solves that problem by creating a new algorithm that can learn and adapt quickly while still being accurate. |
Keywords
» Artificial intelligence » Hyperparameter » Online learning » Optimization
